GitHubIssueViewer

This component fetches and parses GitHub issues into Haystack documents.

Most common position in a pipeline	Right at the beginning of a pipeline and before a ChatPromptBuilder that expects the content of a GitHub issue as input
Mandatory run variables	url: A GitHub issue URL
Output variables	documents: A list of documents containing the main issue and its comments
API reference	GitHub
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/github
Package name	github-haystack

Overview

GitHubIssueViewer takes a GitHub issue URL and returns a list of documents where:

• The first document contains the main issue content
• Subsequent documents contain the issue comments (if any)

Each document includes rich metadata such as the issue title, number, state, creation date, author, and more.

Authorization

The component can work without authentication for public repositories, but for private repositories or to avoid rate limiting, you can provide a GitHub personal access token.

Pass the token during initialization via the github_token parameter, for example github_token=Secret.from_env_var("GITHUB_TOKEN"). This component has no default environment variable for the token.

To create a personal access token, visit GitHub's token settings page.

Installation

Install the GitHub integration with pip:

shell

pip install github-haystack

Usage

Repository Placeholder

To run the following code snippets, you need to replace the owner/repo with your own GitHub repository name.

On its own

Basic usage without authentication:

python

from haystack_integrations.components.connectors.github import GitHubIssueViewer

viewer = GitHubIssueViewer()
result = viewer.run(url="https://github.com/deepset-ai/haystack/issues/123")

print(result)

bash

{'documents': [Document(id=3989459bbd8c2a8420a9ba7f3cd3cf79bb41d78bd0738882e57d509e1293c67a, content: 'sentence-transformers = 0.2.6.1
haystack = latest
farm = 0.4.3 latest branch

In the call to Emb...', meta: {'type': 'issue', 'title': 'SentenceTransformer no longer accepts \'gpu" as argument', 'number': 123, 'state': 'closed', 'created_at': '2020-05-28T04:49:31Z', 'updated_at': '2020-05-28T07:11:43Z', 'author': 'predoctech', 'url': 'https://github.com/deepset-ai/haystack/issues/123'}), Document(id=a8a56b9ad119244678804d5873b13da0784587773d8f839e07f644c4d02c167a, content: 'Thanks for reporting!
Fixed with #124 ', meta: {'type': 'comment', 'issue_number': 123, 'created_at': '2020-05-28T07:11:42Z', 'updated_at': '2020-05-28T07:11:42Z', 'author': 'tholor', 'url': 'https://github.com/deepset-ai/haystack/issues/123#issuecomment-635153940'})]}

In a pipeline

The following pipeline fetches a GitHub issue, extracts relevant information, and generates a summary:

python

from haystack import Pipeline
from haystack.components.builders.chat_prompt_builder import ChatPromptBuilder
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage
from haystack_integrations.components.connectors.github import GitHubIssueViewer

# Initialize components
issue_viewer = GitHubIssueViewer()

prompt_template = [
    ChatMessage.from_system("You are a helpful assistant that analyzes GitHub issues."),
    ChatMessage.from_user(
        "Based on the following GitHub issue and comments:\n"
        "{% for document in documents %}"
        "{% if document.meta.type == 'issue' %}"
        "**Issue Title:** {{ document.meta.title }}\n"
        "**Issue Description:** {{ document.content }}\n"
        "{% else %}"
        "**Comment by {{ document.meta.author }}:** {{ document.content }}\n"
        "{% endif %}"
        "{% endfor %}\n"
        "Please provide a summary of the issue and suggest potential solutions.",
    ),
]

prompt_builder = ChatPromptBuilder(template=prompt_template, required_variables="*")
llm = OpenAIChatGenerator(model="gpt-4o-mini")

# Create pipeline
pipeline = Pipeline()
pipeline.add_component("issue_viewer", issue_viewer)
pipeline.add_component("prompt_builder", prompt_builder)
pipeline.add_component("llm", llm)

# Connect components
pipeline.connect("issue_viewer.documents", "prompt_builder.documents")
pipeline.connect("prompt_builder.prompt", "llm.messages")

# Run pipeline
issue_url = "https://github.com/deepset-ai/haystack/issues/123"
result = pipeline.run(data={"issue_viewer": {"url": issue_url}})

print(result["llm"]["replies"][0])